Methods and apparatus for narrow band interference detection and suppression in ultra-wideband systems

ABSTRACT

An exemplary method is disclosed to accurately estimate the center frequency of a narrow-band interference (NBI). The exemplary method uses multi-stage autocorrelation-function (ACF) to estimate an NBI frequency. The exemplary method allows an accurate estimation of the center frequency of NBI in an Ultra-Wideband system. A narrow band interference (NBI) estimator based on such a method allows a low complexity hardware implementation. The exemplary method estimates the frequency in multiple stages. Each stage performs an ACF operation on the received signals. The first stage gives an initial estimation and the following stages refine the estimation. The results of all stages are combined to produce the final estimation. An apparatus based on such a multi-stage narrow band interference frequency detector is also disclosed to improve the accuracy by combining various filters with the detector.

TECHNICAL FIELD

The disclosure relates to wireless communication systems, and moreparticularly, to detecting and suppressing narrow band interference(NBI) in Ultra-Wideband (UWB) systems.

BACKGROUND INFORMATION

The Ultra-Wideband (UWB) technology can be used in many systemsincluding high data-rate, short-range wireless personal network (WPAN)as well as highly accurate localization systems. There are three basictechnologies: Multi-band orthogonal frequency division multiplexing(MB-OFDM) based, impulse radio based and direct spread spectrum sequence(DSSS). There are published international standards for communicationsystems based on UWB technologies which include ECMA-368, IEEE 802.15.4aetc.

A UWB system occupies a large bandwidth (>500 MHz) and therefore theprobability of the existence of an in-band narrow-band interference ishigh. In addition, the signal power of the NBI is typically much higherthan the UWB signal power. Therefore NBI causes significant performancedegradation of the UWB system.

Conversely, a UWB system also becomes the interference source to narrowband systems. In many countries and regions, regulations require thatUWB systems must be able to detect the existence of narrow band systemsand avoid transmission on the frequencies occupied by the narrow bandsystems.

To guarantee the performance of UWB systems under NBI, it is importantfor a UWB transceiver to remove or reduce the power level of the NBI. Tobe able to detect the presence of NBI and estimate its frequencyaccurately is important in order to design a UWB transceiver with NBIcancellation/rejection capability.

As a majority of the UWB systems are projected to be used inapplications where nodes are mobile, low cost and battery powered, it isessential that NBI detection/cancellation can be implemented in lowcomplexity, low power hardware.

SUMMARY

Exemplary methods and program products are disclosed to accuratelyestimate the center frequency of a narrow-band interference (NBI). Suchexemplary methods and program products use a multi-stageautocorrelation-function (ACF) to estimate an NBI frequency. Theexemplary method allows an accurate estimation of the center frequencyof NBI in a UWB system. A narrow band interference (NBI) estimator basedon such a method allows a low complexity hardware implementation.

An exemplary multi-stage narrow band interference frequency detectorestimates the frequency in multiple stages. Each stage performs ACFoperation on the received signals. The first stage gives an initialestimation and the following stages refine the estimation. The resultsof all stages are combined to produce the final estimation.

Various exemplary methods, receivers and apparatus are disclosed toimprove the accuracy by combining various exemplary receivers andadaptive filters with the aforementioned exemplary narrow-bandinterference (NBI) estimator.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary power spectrum density (PSD) of two signals.Ps(f) is the PSD of an Ultra-Wideband (UWB) signal S(t) with centerfrequency f_(c) and bandwidth B_(u);

FIG. 2 is a the block diagram of a known UWB receiver;

FIG. 3 is a block diagram of an exemplary embodiment of a narrow-bandinterference (NBI) frequency detector which outputs an estimate of theNBI frequency {circumflex over (f)}_(i);

FIG. 4 shows the simulated results of the performance of the exemplaryembodiment of an NBI detector;

FIG. 5 shows an exemplary embodiment which combines an NBI detector withan adaptive filter (AF) to further improve the frequency estimationaccuracy;

FIG. 6 shows a UWB receiver 600 equipped with an exemplary embodiment ofan NBI detector and an adjustable notch filter; and

FIG. 7 show a UWB system and a NB system colocated with each other,wherein the narrow band system signal is I(t) and the UWB signal isS(t).

DETAILED DESCRIPTION

FIG. 1 shows an exemplary power spectrum density (PSD) of two signals.Ps(f) 102 is the PSD of an Ultra-Wideband (UWB) signal S(t) with centerfrequency f_(c) 110 and bandwidth B_(u). The UWB signal spans fromf_(c)−B_(u)/2 to f_(c)+B_(u)/2. The maximum signal power density isbelow the limit by regulations (e.g., −41 dBm/MHz in the U.S.). Thenarrow band signal I(t) has a spectrum of P_(i)(f) 101. Its centerfrequency is f_(c)+f_(i) 120 and the bandwidth B_(i) satisfiesB_(i)<<B_(u). Here f_(i) is the offset between the center frequencies ofthe UWB signal and the NB signal. The power of the narrow band signalcan be significantly higher than the power of the UWB signals. Forexample, the strength of an IEEE 802.11a signal can be as high as 20dBm, where IEEE 802.11a is a standard that specifies an OFDM physicallayer that splits an information signal across separate subcarriers toprovide transmission of data.

FIG. 2 is a block diagram of a known UWB receiver 400. The radiofrequency (RF) front end 410 converts the radio frequency signalR_(RF)(t) 421 down to analog baseband signal R_(B)(t) 411. While ananalog to digital converter (ADC) is exemplified to further convert thesignal to digital format, any variant of discrete received signal isrepresented by R(n) 401 with sampling interval of T (sampling frequencyof f=1/T) based on f_(CLK) 431. The digital functional blocks includesynchronization (Sync), channel estimation, data demodulation, etc. Areceiver typically also includes the automatic gain control (AGC)circuit to control a variable gain amplifier (VGA). The AGC circuit ispartially or entirely implemented in the analog circuit.

At the presence of narrow-band interference (NBI), the discrete receivedsignal R(n) 401 is the sum of the UWB signal S(n) and the interferencesignal I(n). R(n)=S(n)+I(n).

FIG. 3 is a block diagram of an exemplary embodiment of an NBI frequencydetector. Such an NBI frequency detector 200 outputs an estimate of theNBI frequency {circumflex over (f)}_(i) 202. Such an exemplaryembodiment of an NBI frequency detector acquires and refines theestimation of NBI central frequency in multiple iterations (e.g., shownin FIG. 3 are two exemplary stages, each stage including a respectiveACF unit 210 or 220).

The 1^(st) stage (including a first ACF unit 210) produces an initialfrequency estimate. Each following stage estimates the residue frequencywith respect to the previous estimate. The output is a weightedcombining of the estimation of all stages. The mathematical expressionof the estimator output is

$\begin{matrix}{{\hat{f}}_{i} = {\sum\limits_{g = 1}^{S}{\beta_{g}{\hat{f}}_{i,g}}}} & (1)\end{matrix}$

where S is the total number of stages, {circumflex over (f)}_(i,g) isthe g^(th) stage estimate and β_(g) is a combining weight of the g^(th)stage. β₁ is always set to 1, β_(g) are values in [0,1] for g>1 andgenerally β_(g)=1.

The estimated frequency of the first stage (e.g., the output 235represented by {circumflex over (f)}_(i,1) based on an angular-function233) is given as

$\begin{matrix}{{\hat{f}}_{i,1} = {\frac{1}{2\pi \; T}\arg \left\{ {{ACF}\left( {{m;M},1} \right)} \right\}}} & (2)\end{matrix}$

and the frequency estimate of the g^(th) stage (e.g., the output 236represented by {circumflex over (f)}_(i,g) based on an angular function234) where g>1 is given as;

$\begin{matrix}{{\hat{f}}_{i,g} = {\frac{1}{2\pi \; K_{g}T}\arg \left\{ {{{ACF}\left( {{m;M},K_{g}} \right)}{\exp\left( {{- j}\; 2\pi \; K_{g}T{\sum\limits_{n = 1}^{g - 1}\; {\hat{f}}_{i,n}}} \right)}} \right\}}} & (3)\end{matrix}$

where

$\arg \left\{ {{{ACF}\left( {{m;M},K_{g}} \right)}{\exp\left( {{- j}\; 2\pi \; K_{g}T{\sum\limits_{n = 1}^{g - 1}\; {\hat{f}}_{i,n}}} \right)}} \right\}$

is the angular function that returns the angle of a complex number;ACF(m;M,K) is the autocorrelation function (ACF) defined as

$\begin{matrix}{{{ACF}\left( {{m;M},K} \right)} = {\sum\limits_{l = 0}^{M - 1}\; {{R\left( {m + l} \right)}{R^{*}\left( {m + l + K} \right)}}}} & (4)\end{matrix}$

where R(m+l) is the discrete received signal R at time instant m+l, mbeing the index of the first sample in the first segment, and l being anoffset index; ( )* denotes the conjugation operator; K is referred as“lag”; and M is the “summation window size” of the ACF. Likewise,ACF(m;M,K_(g)) is an autocorrelation function based on parameters m, Mand K_(g) (the “lag” of stage g).

In an exemplary embodiment, a baseband signal can be sampled at, e.g.,Nyquist frequency (which is generally true for a UWB transceiver),therefore the sampling interval of the ADC of the UWB receiver isT=1/B_(u). An in-band NBI center frequency can be any value in the range[−B_(u)/2, B_(u)/2]. In order to detect NBI of any frequency in[−B_(u)/2, B_(u)/2], the first stage ACF lag must be 1 sample. The lagK_(S) of the final stage (K_(g), where g=S) shall satisfymax(B_(i))<1/K_(S)T. max(B_(i)) indicates the maximum bandwidth of thedetectable NBI. Also the lag K_(g) of stage g needs to increase with g.

A detailed description of an exemplary embodiment of a 2-stage NBIfrequency detector will be provided with reference to the detector 200illustrated in FIG. 3. The discrete input signal R(n) 201 includes boththe discrete (digitized) UWB signal S(n) and the discrete interferencesignal I(n). R(n)=S(n)+I(n). There are two ACF units in the detector200. The first ACF unit 210 provides a recursive implementation ofACF(m;M,1) as follows,

$\begin{matrix}\begin{matrix}{{{ACF}\left( {{m;M},1} \right)} = {\sum\limits_{l = 0}^{M - 1}\; {{R\left( {m + l} \right)}{R^{*}\left( {m + l + 1} \right)}}}} \\{= {{{R\left( {m + M - 1} \right)}{R^{*}\left( {m + M} \right)}} - {{R\left( {m - 1} \right)}{R^{*}(m)}} +}} \\{{{ACF}\left( {{{m - 1};M},1} \right)}}\end{matrix} & (5)\end{matrix}$

The 2^(nd) ACF unit 220 performs a recursive implementation ofACF(m;M,K₂).

The ACF stages shown in FIG. 3 include delay elements 211, 221, complexmultipliers 250 and adders 251.

The angle of the 1^(st) stage ACF output 219 is {circumflex over(f)}_(i,1) 235. The 2^(nd) stage ACF output 229 is rotated by−j2πT{circumflex over (f)}_(i,1), (See, e.g., a complex exponential 232driving a complex multiplier 250.) The angle of the rotated output isthe estimated residual phase rotation and the residual frequencyestimate is {circumflex over (f)}_(i,2) 236 as expressed in Equation(3).

The estimated frequency {circumflex over (f)}_(i) 202 is the weighedcombining of both stages {circumflex over (f)}_(i)={circumflex over(f)}_(i,1)+β₂·{circumflex over (f)}_(i,2). For an exemplary two-stageembodiment shown in FIG. 3, the estimated frequency 202 is a combinedoutput {circumflex over (f)}_(i) based on the first output {circumflexover (f)}_(i,1) 235 and a weighted second output β₂·{circumflex over(f)}_(i,2).

A Systems Analysis of an Exemplary Two-Stage NBI Detector in WiMediaMB-OFDM UWB System

Referring to the exemplary embodiment of an NBI frequency detector 200as shown in FIG. 3, computational processes otherwise expressed ascomplex equations as follows can be described for such exemplary stages,units or process elements amenable to systems implementation andanalysis as exemplified. Such computational processes as otherwiseexpressed in analytical expressions can be variously implemented insystems and discrete logic, such as digital or analog logic for signalprocessing, or as executable instructions of a computer program orprogram product embodied in any computer readable medium for use by orin connection with an instruction execution system, apparatus, ordevice, such as a computer-based system, digital signal processor, ASICor FPGA devices, or other processors or systems that can fetch theinstructions from the instruction execution system, apparatus, or deviceand execute the instructions.

As used here, a “computer readable medium” can be any readable mediumfor use by or in connection with the instruction execution system,apparatus, or device. The computer readable medium can be based on asystem, apparatus, device, or a removable storage device; and caninclude an electrical connection having one or more wires, a portablecomputer diskette, a random access memory (RAM), a read only memory(ROM), an erasable programmable read only memory (EPROM or Flashmemory), an optical fiber, and a portable compact disc read only memory(CDROM).

As shown in FIG. 3, an exemplary embodiment of an NBI frequency detector200 is used to output an estimate 202 of the NBI frequency {circumflexover (f)}_(i), wherein a sampled received NBI signal 201 can berepresented as Ĩ[m]=I(m)+v(m), whereI[m]=A_(i)b[m]exp(j(2πf_(i)mT+φ_(i))) is the discrete narrow band signaland v[m] is the discrete noise sample with the variance of σ². Note theUWB signal is considered a component in v[m]. Other noise includesthermal noise.

The autocorrelation (e.g., an output 219 based on a first ACF unit 210)of the 1^(st) stage, amenable to computational logic for signalprocessing, can be expressed as

${{{ACF}\left( {{m:M},1} \right)} = {{\sum\limits_{m}\; {{\overset{\sim}{I}\lbrack m\rbrack}{\overset{\sim}{I}\left\lbrack {m + 1} \right\rbrack}}} = {{\sum\limits_{m}\; {{A_{i}^{2}\left( {{b^{*}\lbrack m\rbrack}{b\left\lbrack {m + 1} \right\rbrack}} \right)}^{j\; 2\pi \; f_{i}T}}} + {v_{i,1}\mspace{14mu} {where}}}}}\mspace{14mu}$$v_{i,1} = {{\sum\limits_{m}\; {{I^{*}\lbrack m\rbrack}{v\left\lbrack {m + 1} \right\rbrack}}} + {\sum\limits_{m}\; {{I\lbrack m\rbrack}{v^{*}\left\lbrack {m + 1} \right\rbrack}}} + {\sum\limits_{m}\; {{v^{*}\lbrack m\rbrack}{v\left\lbrack {m + 1} \right\rbrack}\mspace{14mu} {is}}}}$

the composite noise term. Since the coherent time of NB signal T_(i)>>T,b*[m]b[m+1]≈|b[m]|². The NBI frequency estimation (e.g., the output 235represented by {circumflex over (f)}_(i,1) based on an angular function233) at the first stage is thus given by

${\hat{f}}_{i,1} = {\frac{1}{2\pi \; T}\arg \left\{ {\sum\limits_{m}\; {{\overset{\sim}{I}\lbrack m\rbrack}{\overset{\sim}{I}\left\lbrack {m + 1} \right\rbrack}}} \right\}}$

In the 2^(nd) stage, let K₂=10 for example. Taking an example of 1/T=528MHz, we have B_(i,max)=52.8 MHz, which holds for most existingnarrowband systems. The 2^(nd) stage ACF output (e.g., an output 229based on a 2^(nd) ACF unit 220), amenable to computational logic forsignal processing, can be expressed as

${{ACF}\left( {{m:M},K_{2}} \right)} = {{\sum\limits_{m}\; {{\overset{\sim}{I}\lbrack m\rbrack}{\overset{\sim}{I}\left\lbrack {m + K_{2}} \right\rbrack}}} = {{\sum\limits_{m}\; {{A_{i}^{2}\left( {{b^{*}\lbrack m\rbrack}{b\left\lbrack {m + K_{2}} \right\rbrack}} \right)}^{j\; 2\pi \; f_{i}K_{2}T}}} + {v_{i,{K\; 2}}.}}}$

Again, v_(i,k2) is the composite noise term and the NB signal is stillcoherent with lag of K₂ and therefore b*[m]b[m+K₂]≈|b[m]|².

Rotating (e.g., a complex exponential 232 driving a complex multiplier250) the 2^(nd) stage ACF output by exp(−j2πK₂T{circumflex over(f)}_(i,1)), the phase of the rotated vector is 2πK₂T(f_(i)−{circumflexover (f)}_(i,1)). The corresponding estimation (e.g., the output 236represented by {circumflex over (f)}_(i,g) based on an angular function234) on the residue {circumflex over (f)}_(i,2)=f_(i)−{circumflex over(f)}_(i,1) is thus given by

${\hat{f}}_{i{.2}} = {\frac{1}{2\pi \; K_{2}T}\arg \left\{ {\sum\limits_{m}\; {{\overset{\sim}{I}\lbrack m\rbrack}{\overset{\sim}{I}\left\lbrack {m + K_{2}} \right\rbrack}{\exp \left( {{- j}\; 2\pi {\hat{f}}_{i,1}K_{2}T} \right)}}} \right\}}$

The frequency estimate 202 of the two-stage NBI detector is given as{circumflex over (f)}_(i)={circumflex over (f)}_(i,1)+β₂·{circumflexover (f)}_(i,2). If we let β₂=1 (as represented by β₂ 231), theestimated frequency 202 becomes {circumflex over (f)}_(i)={circumflexover (f)}_(i,1)+{circumflex over (f)}_(i,2).

Systems Embodiments, Results and Effects of Frequency Estimation for aWiMedia's MB-OFDM UWB System

Table 1 and FIG. 4 show the frequency estimation error under differentinterference to noise ratio (INR) for a WiMedia MB-OFDM UWB system. Inthe simulation, M=160, K₂=10 (therefore B_(i,max)=52.8 MHz). β₂ is setto 1. The estimation error is normalized to subcarrier spacing (i.e.,4.125 MHz). The results show that for INR at 1 dB, the frequencyestimation error is less than 3 subcarriers with 95% confidence. For INRof 5 dB or higher, the frequency estimation error is within 1 subcarrierwith 95% confidence.

TABLE 1 Simulation results of frequency estimation error INR (dB) in CM1Channels in CM4 Channels −9 13.4311 ± 1.6290  15.8030 ± 1.8229  −47.5120 ± 1.3621 7.4037 ± 1.3976 1 2.7367 ± 0.9576 2.7944 ± 0.9604 60.1887 ± 0.2503 0.5744 ± 0.4905 11 0.1566 ± 0.2515 0.1579 ± 0.2512 160.0186 ± 0.0015 0.1470 ± 0.2513

FIG. 5 illustrates an exemplary embodiment 500 of the disclosedstructure in which a NBI detector 200 is used in combination with a3-tap adaptive filter 444. Although a 3-tap adaptive filter 444 isexemplified, a larger multi-tap adaptive filter can be used. As shown,the discrete received signal R(n) is split into two signal paths, thefirst path being processed by a 3-tap adaptive filter 444 and the secondpath leading to an adder. The difference between the second path R(n)and the adaptively filtered path R_(o)(n) results in an error signalwhich is used in the feedback loop to adapt the 3-tap adaptive filter444 based on parameter settings to emphasize the NBI signal and suppressthe UWB signal level of the resulting adaptively filtered signalR_(o)(n). The signal R_(o)(n) is fed to the NBI detector 200 to yield anestimated frequency {circumflex over (f)}_(i). This allows the NBIdetector to estimate the NBI frequency more accurately.

FIG. 6 gives an example of a UWB receiver 600 equipped with an exemplaryembodiment of an NBI detector 200 and an adjustable notch filter 610.The estimated frequency 202 by the NBI detector is used to tune anadjustable/programmable notch filter. The combination of the NBIdetector 200 and the adjustable notch filter 610 can remove or reducethe power level of the narrow band interference with arbitrary frequencywhile letting the UWB signal pass through. Even though in the example inFIG. 6 a baseband notch filter is used, it is possible to use a notchfilter in RF band and still achieve the same objective of suppressingthe NBI. The NBI detector 200 and the adjustable notch filter 610 cancancel/suppress NBI adaptively.

The exemplary NBI detection method can also be used to facilitate theimplementation of Detect And Avoid (DAA) in a UWB system. In a DAAenabled systems, UWB nodes need to detect the presence of the narrowband signals and must avoid transmitting in the same frequency as thenarrow band signals. For example, in an MB-OFDM UWB system with NBIdetection capability, once a node detects the NBI and its frequency, itmakes sure the corresponding tones are nullified in its own transmittedsignal. It can also send this NBI frequency information to other UWBdevices in the system so other nodes also nullify the tones. Such afurther exemplary NBI detection method is also encompassed by thepresent disclosure.

FIG. 7 show an example of a UWB system and a NB system colocated witheach other. The narrow band system signal is I(t) and the UWB signal isS(t). In the example, the UWB nodes 701 communicate with each other bytransmitting UWB signals S(t). The narrow band nodes 702 communicatewith each other by transmitting narrow band signal I(t). I(t) is narrowband interference to the UWB transceivers 701. S(t) becomes interferenceto the narrow band transceiver 702.

The various exemplary methods can reliably estimate the frequency ofnarrow band interference accurately with high confidence, especially athigh interference to noise ratio.

The various exemplary methods can operate in the time domain andtherefore can respond faster to NBI than known methods that requireoperations in the frequency domain.

The various exemplary methods can reduce the effect of narrow-bandinterference on the performance of a UWB system.

The various exemplary methods can facilitate the detection and avoidance(DAA) implementation in UWB systems.

The various exemplary methods can be implemented in hardware with lowcomplexity, resulting in low energy consumption.

Although the disclosure has been described by way of examples ofexemplary embodiments, it is to be understood that various otheradaptations and modifications can be made within the spirit and scope ofthe invention. Therefore, it is the object of the appended claims tocover all such variations and modifications as come within the truespirit and scope of the invention.

1. A computational method for narrow-band interference detection basedon discrete signal processing in a radio frequency receiver, the methodcomprising: initially estimating a narrow-band interference frequency ina narrow-band interference frequency detector using a first stage of amulti-stage autocorrelation-function applied to an input signal;estimating at least one residue frequency based on at least one otherautocorrelation stage of the multi-stage autocorrelation-function, theinput signal and the initial frequency estimate; and combining theresults of multiple stages of the multi-stage autocorrelation-functionfor final estimation, wherein the final estimation produces an estimateof the narrow-band interference frequency based on the combined resultsof the multi-stage autocorrelation-function.
 2. The method according toclaim 1, wherein the narrow-band interference frequency being estimatedis a center frequency of the narrow-band interference.
 3. The methodaccording to claim 1, wherein each stage of the multi-stageautocorrelation-function is based on processing anautocorrelation-function computing operation on a received signal.
 4. Anarrow band interference estimator apparatus based on a hardwareimplementation of a process embodying the method according to claim 1.5. The narrow band interference estimator according to claim 4, furtherincludes an adaptive filter combined with the narrow band interferenceestimator.
 6. A multi-stage narrow band interference frequency detector,comprising: a first autocorrelation stage to produce an initialfrequency estimate of a narrow band interference based on an inputsignal; and at least one other autocorrelation stage to estimate aresidue frequency based on the input signal and the initial frequencyestimate, wherein an output estimate {circumflex over (f)}_(i) of themulti-stage narrow band interference frequency detector is based on acombination of the initial frequency estimate of a narrow bandinterference and the at least one estimate of a residue frequency. 7.The multi-stage narrow band interference frequency detector according toclaim 6, wherein the output estimate {circumflex over (f)}_(i) iscomputationally expressed as${\hat{f}}_{i} = {\sum\limits_{g = 1}^{S}\; {\beta_{g}{\hat{f}}_{i,g}}}$where S is the total number of stages, {circumflex over (f)}_(i,g) isthe g^(th) stage estimate and β_(i) is a combining weight of the g^(th)stage.
 8. The multi-stage narrow band interference frequency detectoraccording to claim 6, wherein the initial frequency estimate of thefirst autocorrelation stage is computationally expressed as${\hat{f}}_{i,1} = {\frac{1}{2\pi \; T}\arg \left\{ {{ACF}\left( {{m;M},1} \right)} \right\}}$wherein arg{ACF(m;M,1)} is the angular function that returns the angleof a complex number, and wherein ACF(m;M,1) is the autocorrelationfunction (ACF) defined as${{ACF}\left( {{m;M},1} \right)} = {\sum\limits_{l = 0}^{M - 1}\; {{R\left( {m + l} \right)}{R^{*}\left( {m + l + 1} \right)}}}$where R(m+l) is a discrete received signal R at time instant m+l, mbeing an index of a first sample in a first segment, and l being anoffset index; ( )* denotes a conjugation operator; and M is summationwindow size of the ACF.
 9. The multi-stage narrow band interferencefrequency detector according to claim 6, wherein the residue frequencyestimate of the at least one other autocorrelation stage g, where g>1,is computationally expressed as${\hat{f}}_{i,g} = {\frac{1}{2\pi \; K_{g}T}\arg \left\{ {{{ACF}\left( {{m;M},K_{g}} \right)}{\exp\left( {{- j}\; 2\pi \; K_{g}T{\sum\limits_{n = 1}^{g - 1}\; {\hat{f}}_{i,n}}} \right)}} \right\}}$where$\arg \left\{ {{{ACF}\left( {{m;M},K_{g}} \right)}{\exp\left( {{- {j2\pi}}\; K_{g}T{\sum\limits_{n = 1}^{g - 1}\; {\hat{f}}_{i,n}}} \right)}} \right\}$is the angular function that returns the angle of a complex number;ACF(m;M,K_(g)) is the autocorrelation function (ACF) defined as${{ACF}\left( {{m;M},K_{g}} \right)} = {\sum\limits_{l = 0}^{M - 1}\; {{R\left( {m + l} \right)}{R^{*}\left( {m + l + K_{g}} \right)}}}$where R(m+l) is a discrete received signal R at time instant m+l, mbeing an index of a first sample in a first segment, and l being anoffset index; ( )* denotes a conjugation operator; K_(g) is the “lag” ofstage g; and M is summation window size of the ACF.
 10. The multi-stagenarrow band interference frequency detector according to claim 9,wherein the lag K_(S) of the final stage (K_(g), where g=S) satisfiesmax(B_(i))<1/K_(S)T; max(B_(i)) indicates the maximum bandwidth of thedetectable narrow band interference; and the lag K_(g) of stage gincreases with the value of g.
 11. The multi-stage narrow bandinterference frequency detector according to claim 6, wherein a sampledbaseband signal is used as the input signal, and wherein the outputestimate {circumflex over (f)}_(i) of the multi-stage narrow bandinterference frequency detector is a weighted combination of the initialfrequency estimate of a narrow band interference and the at least oneestimate of a residue frequency.
 12. The multi-stage narrow bandinterference frequency detector according to claim 11, wherein thesampled baseband signal has a sampling interval of T=1/B_(u), andwherein a center frequency narrow band interference can be any value inthe range [−B_(u)/2, B_(u)/2].
 13. An apparatus based on a multi-stagenarrow band interference frequency detector, comprising: a 3-tapadaptive filter receiving the input signal to output an adaptivelyfiltered signal R_(o)(n), a difference of the input signal and theadaptively filtered signal R_(o)(n) resulting in an error signal whichis used in a feedback loop to adapt the 3-tap adaptive filter based onparameter settings to emphasize a NBI signal and suppress a UWB signallevel of the resulting adaptively filtered signal R_(o)(n); and themulti-stage narrow band interference frequency detector according toclaim 6 connected to the 3-tap adaptive filter to receive the adaptivelyfiltered signal R_(o)(n), and output an improved estimate {circumflexover (f)}_(i) of the narrow band interference frequency.
 14. A receiverapparatus based on a multi-stage narrow band interference frequencydetector, comprising: an adjustable or programmable notch filterreceiving the input signal; and the multi-stage narrow band interferencefrequency detector according to claim 6 connected to the adjustablenotch filter, wherein the output estimate {circumflex over (f)}_(i) isused to tune the adjustable or programmable notch filter.
 15. Thereceiver apparatus according to claim 14, wherein the apparatus servesto remove or reduce a power level of the narrow band interference witharbitrary frequency while passing an Ultra-Wideband signal through. 16.The receiver apparatus according to claim 14, wherein the adjustable orprogrammable notch filter is a baseband notch filter to cancel orsuppress the narrow band interference adaptively.
 17. The receiverapparatus according to claim 14, wherein the adjustable or programmablenotch filter is a notch filter in RF band to cancel or suppress thenarrow band interference adaptively.
 18. A computer-readable storagemedium containing a program for causing a signal processor to process asignal for narrow-band interference detection, the process comprising:initially estimating a narrow-band interference frequency using a firststage of a multi-stage autocorrelation-function applied to an inputsignal; estimating at least one residue frequency based on at least oneother autocorrelation stage of the multi-stage autocorrelation-function,the input signal and the initial frequency estimate; and combining theresults of multiple stages of the multi-stage autocorrelation-functionfor final estimation, wherein the final estimation produces an estimateof the narrow-band interference frequency based on the combined resultsof the multi-stage autocorrelation-function.
 19. The computer-readablestorage medium according to claim 18, wherein the narrow-bandinterference frequency being estimated is a center frequency of thenarrow-band interference.
 20. The computer-readable storage mediumaccording to claim 18, wherein each stage of the multi-stageautocorrelation-function is based on processing anautocorrelation-function computing operation on a received signal.